Botnet datasets suffer from the imbalance problem. That said, in most of the publicly available network traffic datasets, the number of botnet class samples is meagre compared to the normal traffic samples. This inequitable distribution of data makes the ML-based detection models less accurate. To address this issue, data undersampling can be adopted. However, it can result in the loss of diversity and representation of normal traffic [1]}. Oversampling can also solve the data imbalance problem to some extent; however, the use of nearest neighbours, and linear interpolation, may not be suitable for the high-dimensional and complex probability distributions [2]}, [3]}. Researchers have tested several oversampling techniques in [4]} to rank the best performing being SMOTE_IPF, ProWSyn and polynom_fit_SMOTE. However, authors in [5]} declared GANs outperforming those three oversamplers in most of the adversarial training of ML classifiers. Hence we can consider GANs as a suitable candidate for data oversampling compared to other synthetic data generation methods.
